Cognitive question answering pipeline blending

ABSTRACT

An answer to a question may selected from answers from a set of answering pipelines. Question answer data can be generated for a question, using a first answering pipeline. Another set of question answer data can be generated for the second question, using the second answering pipeline. The question answer data can include answers and confidence values for each answer. Using a weighting formula and a blending profile for the first answering pipeline, a vote weight can be determined for an answer with the highest confidence value. The same weighting formula and a second blending profile may be used to determine a vote weight for another answer with the highest confidence value. An answer to the question may be selected from the answers, based on the overall highest vote weight.

BACKGROUND

The present relates to computer systems, and more specifically, toquestion answering (QA) systems.

Recent research has been directed to developing question answering (QA)systems designed to receive input questions, analyze them, and returnapplicable answers. These systems may rely on natural languageprocessing, automated reasoning, machine learning, and other advancedtechniques. Using these techniques, QA systems may provide mechanismsfor searching large sources of content and analyzing the content withregard to a given input question in order to determine an answer to thequestion. In some QA systems this may take the form of hypothesisgeneration, scoring, and ranking in order to determine a final set ofone or more output answers. An example of a QA system is IBM's WATSONsystem.

SUMMARY

Embodiments of the present disclosure may be directed toward a methodthat begins when a first set of question answer data is generated. Thedata may be generated using a first answering pipeline, and the questionanswer data may include a first set of answers and a set of firstpipeline confidence values for each answer. A second set of questionanswer data for the question may be generated using a second answeringpipeline. The second set of question answer data may include a secondset of answers and a set of second pipeline confidence values for eachanswer in the second set of answers. Using a weighting formula and afirst blending profile, a first vote weight for an answer in the firstset of question answer data that was assigned a first pipeline highestconfidence value by the first answering pipeline. Using the sameweighting formula and a second blending profile, a second vote weightfor an answer in the second set of question answer data that wasassigned a second pipeline highest confidence value by the second answerpipeline may be determined. The first and second blending profiles maybe associated with the first and second answering pipelines,respectively. The answer with an overall highest vote weight may then beselected from among the answers in the first set of question answer dataand in the second set of question answer data. The answer selected maybe selected as the first answer to the question.

Embodiments of the present disclosure may be directed toward a computerreadable storage medium with program instructions stored thereon, andone or more processors configured to execute the program instructions toperform a method. The method may begin when a first set of questionanswer data is generated. The data may be generated using a firstanswering pipeline, and the question answer data may include a first setof answers and a set of first pipeline confidence values for eachanswer. A second set of question answer data for the question may begenerated using a second answering pipeline. The second set of questionanswer data may include a second set of answers and a set of secondpipeline confidence values for each answer in the second set of answers.Using a weighting formula and a first blending profile, a first voteweight for an answer in the first set of question answer data that wasassigned a first pipeline highest confidence value by the firstanswering pipeline. Using the same weighting formula and a secondblending profile, a second vote weight for an answer in the second setof question answer data that was assigned a second pipeline highestconfidence value by the second answer pipeline may be determined. Thefirst and second blending profiles may be associated with the first andsecond answering pipelines, respectively. The answer with an overallhighest vote weight may then be selected from among the answers in thefirst set of question answer data and in the second set of questionanswer data. The answer selected may be selected as the first answer tothe question.

Embodiments of the present disclosure may be directed toward a computerprogram product with a computer readable storage medium, where thecomputer readable storage medium may have program instructions embodiedtherewith, and it is not a transitory signal per se. The programinstructions may be executed by a computer processing circuit to causethe circuit to perform a method. The method may begin when a first setof question answer data is generated. The data may be generated using afirst answering pipeline, and the question answer data may include afirst set of answers and a set of first pipeline confidence values foreach answer. A second set of question answer data for the question maybe generated using a second answering pipeline. The second set ofquestion answer data may include a second set of answers and a set ofsecond pipeline confidence values for each answer in the second set ofanswers. Using a weighting formula and a first blending profile, a firstvote weight for an answer in the first set of question answer data thatwas assigned a first pipeline highest confidence value by the firstanswering pipeline. Using the same weighting formula and a secondblending profile, a second vote weight for an answer in the second setof question answer data that was assigned a second pipeline highestconfidence value by the second answer pipeline may be determined. Thefirst and second blending profiles may be associated with the first andsecond answering pipelines, respectively. The answer with an overallhighest vote weight may then be selected from among the answers in thefirst set of question answer data and in the second set of questionanswer data. The answer selected may be selected as the first answer tothe question.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 depicts a block diagram of an example computing environment foruse with a question answering system, according to embodiments of thepresent disclosure.

FIG. 2 depicts a block diagram of an example question answering systemusable to generate answers to one or more input questions, according toembodiments of the present disclosure.

FIG. 3 depicts a block diagram of an example high level logicalarchitecture of a QA system, according to embodiments of the presentdisclosure.

FIG. 4 depicts a diagram of an illustrative embodiment of a system forgenerating metadata for a set of QA pipelines, according to embodimentsof the present disclosure.

FIG. 5 depicts a flow diagram of a method for calibrating a set of QApipelines, according to embodiments of the present disclosure.

FIG. 6 depicts a diagram of an illustrative embodiment of a system forblending answers from a set of answering pipelines, according toembodiments of the present disclosure.

FIG. 7 depicts a flow diagram of a method for blending answers from aset of QA pipelines, according to embodiments of the present disclosure.

FIG. 8 depicts a cloud computing environment, according to embodimentsof the present disclosure.

FIG. 9 depicts abstraction model layers, according to embodiments of thepresent disclosure.

FIG. 10 depicts an example computer system, according to embodiments ofthe present disclosure.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to computer systems, moreparticular aspects relate to question answering (QA) systems. While thepresent disclosure is not necessarily limited to such applications,various aspects of the disclosure may be appreciated through adiscussion of various examples using this context.

In cognitive computing, a monolithic question and answer (QA) systemcontaining multiple answering pipelines may be decomposed into separatecognitive services. For example, answering pipelines for different typesof questions may be created as separate cognitive services, in order tofacilitate reuse in different combinations within different cognitivesolutions. Additionally, answering pipelines for the same type ofquestions may be created by separate development teams and operate overseparate data sources. The combination of the pipeline capabilities mayexceed the capabilities of each individual pipeline. In some cases, acognitive solution developer may be expected to utilize a set ofanswering pipeline cognitive services in order to develop asituation-specific solution (e.g., for a particular client).

In some instances, a cognitive solution developer may use a questionclassifier service. A question classifier service may be a systemtrained to distinguish questions of various types. For example, a QAsystem may be trained to answer both questions that require a factoidanswer (e.g., ‘What color is the sky?”) as well as questions thatrequire a descriptive passage answer (e.g., “Why is the sky blue?”). Inusing a question classifier, the QA system may first filter incomingquestions through the question classifier, and, based on a resultantclassification, invoke the appropriate answering pipeline cognitiveservice.

At times, the question classifier may not be able to achieve the desiredaccuracy in distinguishing the intent of the question, due to, forexample, linguistic ambiguity or the brevity of a question. Thus, inmany cases, the question classifier is unable to return a specific classfor the question type, but rather it may return a list of likelyclasses, with confidence values attached to each class. The confidencevalues may indicate the particular confidence of the question classifierthat the question is of the indicated class. In some cases, thecognitive solution developer may simply choose to invoke an answeringpipeline associated with the highest confidence question class, but thismay be less accurate than desired because the selected answeringpipeline may not produce the best answers.

The cognitive solution developer may also experience further challengewhen faced with multiple answering pipeline cognitive services that areintended to answer the same types of questions, but they may havedifferent data sources or may have been trained differently. In somecases, the developer may simply choose the answer or answers from theanswering pipeline that has the highest confidence top answer oranswers, however this may also be less accurate than desired because theselected answering pipeline may not produce the best answers.

Finally, the cognitive solution developer may run each of the applicableanswering pipelines individually, receive the list of the highestconfidence answers from each, and merge the lists into one list, thatmay be sorted by, for example, the highest confidence value assigned toeach (by the different answering pipelines). However, the confidencevalues from the different answering pipelines may be incomparable typesdue to being generated by distinct cognitive models, and thus using theminterchangeably may result in inaccuracies.

In embodiments, the initial “conversion” may occur through a calibrationof the different answering pipelines, using a probabilistic weightedvoting mechanism. A system may first receive a set of test questions tobe processed by each answering pipeline, in order to generate a set ofanswer data for each question. The set of test questions may beprocessed by a first answering pipeline to obtain a set of answers, andtheir confidence values, for each test question. The processing of eachtest question may generate a set of answers and compare them with ananswer key entry associated with the test question to ascertain which ofthe answers are correct for the test question. Using this first set ofanswers, along with their confidence and correctness values, the systemmay generate a set of metadata for the first answering pipeline toreflect various levels of accuracy and confidence probabilities.

In embodiments, the same set of test questions may be processed by asecond answering pipeline. A second set of answer data, including theset of answers as well as their confidence and correctness values, maybe generated. Using this second set of answer data, the system maygenerate another set of metadata for the second answering pipeline toreflect various levels of accuracy and confidence probabilities. Themetadata from the first and second pipelines may be stored in profiles,referred to herein as a “first blending profile” and a “second blendingprofile”, respectively.

In embodiments, a system may receive a question for processing by aplurality of question answering pipelines. In response, the system maygenerate a first set of question answer data using the first pipeline.The question answer data may include a set of answers and a set ofconfidence values. These confidence values (“first pipeline confidencevalues”) may be values assigned to each answer that indicate thepipeline's confidence in the accuracy of the answer. The system may alsogenerate a set of question answer data for the question using the secondanswering pipeline. Like the first set of question answer data, thesecond set of question answer data may include a second set of answersas well as a set of second pipeline confidence values for each answer inthe second set of answers. In embodiments, the system may then transmitthese blending profiles to a user for use in answering a question. Thisuser may be a cognitive solution developer, a user client device, anexternal system, or another user. In embodiments, the system may thenstore the profiles, transmit them to an external system, or communicatethem to the same or another system, as is appropriate for the various QAsystems.

Using the first blending profile and a weighting formula, the system maydetermine a vote weight for each answer in the set of question answerdata. Similarly, using the second blending profile and the sameweighting formula, the system may determine a vote weight for eachanswer in the second set of question answer data. The system can thencompare the vote weights assigned to each answer (from each of thepipelines), and select, from the combined sets, an answer with thehighest vote weight. This answer can be considered the best or highestconfidence answer to the question. In embodiments, the system can returna single answer with a vote weight attached, top answers with voteweights attached, or another result as determined by user configuredsettings, a system administrator, or in another way.

As discussed above, aspects of the disclosure may relate to QA systems.Accordingly, an understanding of the embodiments of the presentinvention may be aided by describing embodiments of these QA systems andthe environments in which these systems may operate. FIGS. 1-3 depictexample natural language processing systems, for example QA systems,which may be integrated with, expanded upon, or otherwise modified,according to embodiments of the present disclosure.

FIG. 1 depicts a block diagram of an example computing environment 100for use with a question answering system, according to embodiments ofthe present disclosure. In some embodiments, the computing environment100 may include one or more remote devices 102, 112 and one or more hostdevices 122. Remote devices 102, 112 and host device 122 may be distantfrom each other and communicate over a network 150 in which the hostdevice 122 comprises a central hub from which remote devices 102, 112can establish a communication connection. Alternatively, the host deviceand remote devices may be configured in any other suitable relationship(e.g., in a peer-to-peer or other relationship).

In some embodiments, the network 150 can be implemented by any number ofany suitable communications media (e.g., wide area network (WAN), localarea network (LAN), Internet, Intranet, etc.). Alternatively, remotedevices 102, 112 and host devices 122 may be local to each other, andcommunicate via any appropriate local communication medium (e.g., localarea network (LAN), hardwire, wireless link, Intranet, etc.). In someembodiments, the network 150 can be implemented within a cloud computingenvironment, or using one or more cloud computing services. Consistentwith various embodiments, a cloud computing environment may include anetwork-based, distributed data processing system that provides one ormore cloud computing services. Further, a cloud computing environmentmay include many computers, hundreds or thousands of them or more,disposed within one or more data centers and configured to shareresources over the network 150.

In some embodiments, host device 122 may include a question answeringsystem 130 having a search module 134 and an answer module 132. Thesearch module may be implemented by a conventional or other searchengine, and may be distributed across multiple computer systems. Thesearch module 134 may be configured to search one or more databases orother computer systems for content that is related to a question inputby a user at a remote device 102, 112.

In some embodiments, remote devices 102, 112 may enable users to submitquestions (e.g., search requests or other user queries) to host devices122 to retrieve search results. For example, the remote devices 102, 112may include a query module 110, 120 (e.g., in the form of a web browseror any other suitable software module) and present a graphical userinterface or other interface (e.g., command line prompts, menu screens,etc.) to solicit queries from users for submission to one or more hostdevices 122 and to display answers/results obtained from the hostdevices 122 in relation to such user queries.

Consistent with various embodiments, host device 122 and remote devices102, 112 may be computer systems, and may each be equipped with adisplay or monitor. The computer systems may include at least oneprocessor 106, 116, 126; memories 108, 118, 128; internal or externalnetwork interface or communications devices 104, 114, 124 (e.g., modem,network cards, etc.); optional input devices (e.g., a keyboard, mouse,or other input device); and any commercially available or customsoftware (e.g., browser software, communications software, serversoftware, natural language processing software, search engine and/or webcrawling software, filter modules for filtering content based uponpredefined criteria, etc.). In some embodiments, the computer systemsmay include servers, desktops, laptops, and hand-held devices. Inaddition, the answer module 132 may include one or more modules or unitsto perform the various functions of embodiments as described below(e.g., receiving an input question, assigning the input question to aquestion category, determining a set of candidate answers, comparingconfidence scores and user feedback to confidence criteria, etc.), andmay be implemented by any combination of any quantity of software and/orhardware modules or units.

FIG. 2 depicts a block diagram of an example question answering systemusable to generate answers to one or more input questions, according toembodiments of the present disclosure. Aspects of FIG. 2 are directedtoward an exemplary system architecture 200, including a questionanswering system 212 to generate answers to user queries (e.g., inputquestions). In some embodiments, one or more users can send requests forinformation to QA system 212 using a remote device (such as remotedevices 102, 112 of FIG. 1). Such a remote device may include a clientapplication 208 which may itself involve one or more entities operableto generate information that is then dispatched to QA system 212 vianetwork 215. QA system 212 may be able to perform methods and techniquesfor responding to the requests sent by the client application 208. Insome embodiments, the information received at QA system 212 maycorrespond to input questions received from users, where the inputquestions may be expressed in a free form and in natural language. Inembodiments, and as described herein, multiple QA systems like QA system212 may exist, the answers from which may be blended as describedherein.

A question (similarly referred to herein as a user query) may be one ormore words that form a search term or request for data, information, orknowledge. A question may be expressed in the form of one or morekeywords. Questions may include various selection criteria and searchterms. A question may be composed of complex linguistic features inaddition to keywords. However, a keyword-based search for answers mayalso be possible. In some embodiments, using restricted syntax forquestions posed by users may be enabled. The use of restricted syntaxmay result in a variety of alternative expressions that assist users inbetter stating their needs.

Consistent with various embodiments, client application 208 may operateon a variety of devices. Such devices may include, but are not limitedto, mobile and handheld devices (e.g., laptops, mobile phones, personalor enterprise digital assistants, and the like), personal computers,servers, or other computer systems that access the services andfunctionality provided by QA system 212. In some embodiments, clientapplication 208 may include one or more components, such as a mobileclient 210. Mobile client 210, acting as an agent of client application210, may dispatch user query requests to QA system 212.

Consistent with various embodiments, client application 208 may alsoinclude a search module 202, either as part of mobile client 210 orseparately, that may perform several functions, including some or all ofthe above functions of mobile client 210 listed above. For example, insome embodiments, search module 202 may dispatch requests forinformation to QA system 212. In some embodiments, search module 202 maybe a client application to QA system 212. Search module 202 may sendrequests for answers to QA system 212. Search module 202 may beinstalled on a personal computer, a server, or other computer system.

In some embodiments, search module 202 may include a search graphicaluser interface (GUI) 204 and session manager 206. In such situations,users may be able to enter questions in search GUI 204. In someembodiments, search GUI 204 may be a search box or other GUI component,the content of which can represent a question to be submitted to QAsystem 212. Users may authenticate to QA system 212 via session manager206. In some embodiments, session manager 206 may keep track of useractivity across sessions of interaction with the QA system 212. Sessionmanager 206 may also keep track of what questions are submitted withinthe lifecycle of a session of a user. For example, session manager 206may retain a succession of questions posed by a user during a session.In some embodiments, answers produced by QA system 212 in response toquestions posed throughout the course of a user session may also beretained. Information for sessions managed by session manager 206 may beshared between computer systems and devices.

In some embodiments, client applications 208 and QA system 212 may becommunicatively coupled through network 215, e.g., the Internet,intranet, or other public or private computer network. In someembodiments, QA system 212 and client application 208 may communicate byusing Hypertext Transfer Protocol (HTTP) or Representational StateTransfer (REST) calls. In some embodiments, QA system 212 may reside ona server node. Client application 208 may establish server-clientcommunication with QA system 212 or vice versa. In some embodiments, thenetwork 215 can be implemented within a cloud computing environment, orusing one or more cloud computing services.

Consistent with various embodiments, QA system 212 may respond to therequests for information sent by client applications 208 (e.g.,questions posed by users). QA system 212 may generate answers to thereceived questions. In some embodiments, QA system 212 may include aquestion analyzer 214, data sources 224, and answer generator 228.Question analyzer 214 may be a computer module that analyzes thereceived questions. Question analyzer 214 may perform various methodsand techniques for analyzing the questions syntactically andsemantically. In some embodiments, question analyzer 214 can parsereceived questions. Question analyzer 214 may include various modules toperform analyses of received questions. For example, computer modulesthat question analyzer 214 may encompass include, but are not limitedto, a tokenizer 216, part-of-speech (POS) tagger 218, semanticrelationship identifier 220, and syntactic relationship identifier 222.In embodiments, question analyzer 214 may include a question classifier,as described herein, in order to identify a type or class to which thequestion belongs.

Consistent with various embodiments, tokenizer 216 may be a computermodule that performs lexical analysis. Tokenizer 216 can convert asequence of characters into a sequence of tokens. A token may be astring of characters typed by a user and categorized as a meaningfulsymbol. Further, in some embodiments, tokenizer 316 can identify wordboundaries in an input question and break the question or any text intoits component parts such as words, multiword tokens, numbers, andpunctuation marks. In some embodiments, tokenizer 216 can receive astring of characters, identify the lexemes in the string, and categorizethem into tokens.

Consistent with various embodiments, POS tagger 218 may be a computermodule that marks up a word in a text to correspond to a particular partof speech. POS tagger 218 can read a question or other text in naturallanguage and assign a part of speech to each word or other token. POStagger 218 can determine the part of speech to which a word correspondsbased on the definition of the word and the context of the word. Thecontext of a word may be based on its relationship with adjacent andrelated words in a phrase, sentence, question, or paragraph. In someembodiments, the context of a word may be dependent on one or morepreviously posed questions. Examples of parts of speech that may beassigned to words include, but are not limited to, nouns, verbs,adjectives, adverbs, and the like. Examples of other part of speechcategories that POS tagger 218 may assign include, but are not limitedto, comparative or superlative adverbs, wh-adverbs, conjunctions,determiners, negative particles, possessive markers, prepositions,wh-pronouns, and the like. In some embodiments, POS tagger 218 may tagor otherwise annotate tokens of a question with part of speechcategories. In some embodiments, POS tagger 218 may tag tokens or wordsof a question to be parsed by QA system 212.

Consistent with various embodiments, semantic relationship identifier220 may be a computer module that can identify semantic relationships ofrecognized entities (e.g., words, phrases, etc.) in questions posed byusers. In some embodiments, semantic relationship identifier 220 maydetermine functional dependencies between entities and other semanticrelationships.

Consistent with various embodiments, syntactic relationship identifier222 may be a computer module that can identify syntactic relationshipsin a question composed of tokens posed by users to QA system 212.Syntactic relationship identifier 222 can determine the grammaticalstructure of sentences, for example, which groups of words areassociated as “phrases” and which word is the subject or object of averb. Syntactic relationship identifier 222 may conform to formalgrammar.

In some embodiments, question analyzer 214 may be a computer module thatcan parse a received user query and generate a corresponding datastructure of the user query. For example, in response to receiving aquestion at QA system 212, question analyzer 214 may output the parsedquestion as a data structure. In some embodiments, the parsed questionmay be represented in the form of a parse tree or other graph structure.To generate the parsed question, question analyzer 214 may triggercomputer modules 216-222. Additionally, in some embodiments, questionanalyzer 214 may use external computer systems for dedicated tasks thatare part of the question parsing process.

Consistent with various embodiments, the output of question analyzer 214may be used by QA system 212 to perform a search of one or more datasources 224 to retrieve information to answer a question posed by auser. In some embodiments, data sources 224 may include data warehouses,information corpora, data models, and document repositories. In someembodiments, the data source 224 may include an information corpus 226.The information corpus 226 may enable data storage and retrieval. Insome embodiments, the information corpus 226 may be a storage mechanismthat houses a standardized, consistent, clean and integrated form ofdata. The data may be sourced from various operational systems. Datastored in the information corpus 226 may be structured in a way tospecifically address reporting and analytic requirements. In someembodiments, the information corpus may be a relational database. Insome example embodiments, data sources 224 may include one or moredocument repositories.

In some embodiments, answer generator 228 may be a computer module thatgenerates answers to posed questions. Examples of answers generated byanswer generator 228 may include, but are not limited to, answers in theform of natural language sentences; reports, charts, or other analyticrepresentation; raw data; web pages; and the like.

Consistent with various embodiments, answer generator 228 may includequery processor 230, visualization processor 232, and feedback handler234. When information in a data source 224 matching a parsed question islocated, a technical query associated with the pattern can be executedby query processor 230. Based on data retrieved by a technical queryexecuted by query processor 230, visualization processor 232 may be ableto render visualization of the retrieved data, where the visualizationrepresents the answer. In some embodiments, visualization processor 232may render various analytics to represent the answer including, but notlimited to, images, charts, tables, dashboards, maps, and the like. Insome embodiments, visualization processor 232 may present the answer tothe user.

In some embodiments, feedback handler 234 may be a computer module thatprocesses feedback from users on answers generated by answer generator228. In some embodiments, users may be engaged in dialog with the QAsystem 212 to evaluate the relevance of received answers. Answergenerator 228 may produce a list of answers (e.g., candidate answers)corresponding to a question submitted by a user. The user may rank eachanswer according to its relevance to the question. In some embodiments,the feedback of users on generated answers may be used for futurequestion answering sessions.

In embodiments, a set of test questions may be used with the QA system112, and the generated answers may be considered the set of testquestion data. Each QA system in a set of QA systems may be calibratedas described herein in order to allow for a relevant comparison betweenthe generated and scored answers from each of their respective answergenerators 228.

The various components of the exemplary question answering systemdescribed above may be used to implement various aspects of the presentdisclosure. For example, the client application 208 could be used toreceive an input question from a user. The question analyzer 214 could,in some embodiments, be used to analyze the input question to determineto which question category the input question should be assigned.Further, the query processor 230 or the answer generator 228 could, insome embodiments, be used to determine a set of candidate answers andcalculate confidence scores for the candidate answers.

FIG. 3 depicts a block diagram of an example high level logicalarchitecture of a QA system, consistent with embodiments of the presentdisclosure. Aspects of FIG. 3 may be directed toward components andmodules for use with a QA system 300. In some embodiments, host device301 and remote device 302 may be embodied by host device 122 and remotedevice 102 of FIG. 1, respectively. In some embodiments, the questionanalysis module 304, located on host device 301, may receive a naturallanguage question (e.g., an input question) from a remote device 302,and can analyze the question to produce information about the questionbased on the question's content and context. This may be accomplished,for example, by using components 216-222 of FIG. 2. The informationproduced by question analysis module 304 may include, for example, thesemantic type of the expected answer. In addition the question analysismodule 304 may assign a question category to the input question andprovide this information to the information source quality controlmodule 314. As used herein question categories may refer to any suitablegroupings of input questions wherein a determination as to theappropriate category for a given question is made at least in part basedon an analysis of the content of the question itself. In someembodiments, a single given question may be included in multiplequestion categories.

Next, the candidate generation module 306 may formulate queries from theoutput of the question analysis module 304 and then pass these querieson to search module 308 which may consult various resources such as theinternet or one or more knowledge resources, e.g., databases or corpora,to retrieve documents that are relevant to answering the user question.As used herein, documents may refer to various types of written,printed, or electronic matter (including passages, web-pages, databasetuples, etc.) that provide information or evidence. As shown in FIG. 3,the search module 308 may consult core information source 310. As usedherein, a core information source may refer to any document or group ofdocuments that is used by a relevant QA system to identify candidateanswers to user questions. The candidate generation module 306 mayextract, from the search results obtained by search module 308,potential (candidate) answers to the question, which it may then score(e.g., with confidence scores) and rank. A final set of candidateanswers, based on a comparison of various confidence scores associatedwith the candidate answers, may then be sent from the candidategeneration module 306 to remote device 302 for presentation to the user.In addition, this information about candidate answers and confidencescores may also be sent to information source quality control module314. A user may respond, via remote device 302, to the candidate answers(for example, by indicating that none of the provided candidate answersare accurate or relevant) through user feedback module 312. The userfeedback module 312 may then provide this feedback to the informationsource quality control module 314.

In some embodiments, the information source quality control module 314may compile and analyze information that it receives during the courseof normal operations of question and answering system 300. This receivedinformation (e.g., information from question analysis module 304,candidate generation module 306, and user feedback module 312) may beusable by the information source quality control module 314 to determinewhether one or more new information sources should be ingested. When theinformation source quality control module 314 determines that a newinformation source having certain characteristics is needed (e.g., aninformation source that is associated with a specific questioncategory), it may instruct an ingestion module 316 accordingly. Based onthese instructions, ingestion module 316 may search one or more remotesources, such as remote corpora 318, in an attempt to locate one or moresuitable new information sources. In some embodiments, once discovered,these new information sources may be ingested by ingestion module 316and become newly ingested information source 320. This informationsource may in turn be analyzed by training module 322. This traininganalysis may take the form of obtaining training candidate answers totraining questions using the newly ingested information source 320 andthen reviewing the quality of these training answers. As used herein,training questions may refer to predetermined questions that are used bya QA system for either (1) reviewing or determining the quality orcharacteristics of an information source used to identify trainingcandidate answers to these questions, (2) creating or refining machinelearning models and routing paths usable by the QA system, or both. Insome embodiments, once a threshold level of confidence in the newinformation source is met, it may be combined with core informationsource 310 and used to answer new input questions as they are receivedfrom users.

The various components and modules of the exemplary high level logicalarchitecture for a QA system described above may be used to implementvarious aspects of the present disclosure. For example, the questionanalysis module 304 may, in some embodiments, be used to obtain inputquestions and assign these input questions to appropriate questioncategories. Further, the candidate generation module 306 and searchmodule 308 may together, in some embodiments, be used to performsearches of core information source 310, generate candidate answers,calculate confidence scores associated with these candidate answer, andprovide these candidate answers to one or more users. Further, theinformation source quality control module 314 may, in some embodiments,be used to analyze confidence scores and determine whether theconfidence scores fail to meet one or more confidence criteria. Further,ingestion module 316 may, in some embodiments, be used to ingest newinformation sources (in response to an indication from the informationsource quality control module 314 that a confidence criterion has notbeen satisfied).

FIG. 4 depicts a diagram of an illustrative embodiment of a system 400for generating metadata for a set of QA pipelines, according toembodiments of the present disclosure. The system 400 may be executedover a computer processor system, or over a series of processorsincluding those connected locally, on the cloud, or in another way. Aset of test questions 402 may be received by the system forpreprocessing 404. In embodiments, the test questions 402 may be a setof one or more test questions generated by the system, by a subjectmatter expert, or in another way. Each question in the set of testquestions 402 may have an associated entry in an answer key, whichindicates the correct or most correct answers to the particularquestion.

As part of preprocessing 404, the system may send the questions, per406, to each of the answering pipelines 412 a, 412 b, and 412 c. In someembodiments, a user or other process within the system may assign thequestion to a particular answering pipeline or set of answeringpipelines. In other embodiments, the test question may be sent withoutprevious sorting, to each of the answering pipelines 412. The questionmay be processed by each answering pipeline 412, and the answeringpipeline 412 can return a set of answer data. This answer data caninclude a set of answers to the question as well as a confidence valuefor each answer. The confidence value may indicate the answer pipeline'sestimate of the likelihood that the particular answer is a suitableanswer to the test question.

The system can then determine an answer rating 408 for each answer,based on the returned set of test answer data from each answeringpipeline 412. In embodiments, the answer rating can be a determinationof whether or not the answer is considered correct, based on acomparison of the returned answer with an answer key entry. The contentof the answer key may have previously been created by a subject matterexpert. In embodiments, the same answer key can be used for the accuracydetermination of each pipeline.

Using the confidence values and answer ratings of the answers, thesystem can then generate metadata 410 to create a blending profile 414a-c for each of the answering pipelines 412. The metadata calculated foreach profile 414 can include an “answer accuracy value”, an “answerconfidence table”, and a “correct answer confidence table”.

The first metadata value to be generated for the blending profile for aparticular answering pipelines (e.g., answering pipeline 412 a), may bethe answer accuracy value. The answer accuracy value may indicate theprobability that the particular answering pipeline (for example,answering pipelines 412 a) will produce the correct answer to a questionit receives.

The second metadata value set to be generated for the blending profilefor a particular answering pipelines (e.g., answering pipeline 412 a),may be the answer confidence table. The answer confidence table maycomprise values that indicate a probability that the answering pipelines(e.g., answering pipeline 412 a) will produce an answer that has aconfidence of at least a particular confidence value.

The third metadata value set to be generated for the blending profilefor a particular answering pipeline (e.g., answering pipeline 412 a) maybe the correct answer confidence table. This table can indicate theprobability that the answering pipeline will produce a correct answerthat has a confidence of at least a particular confidence value.

Metadata can be generated for each answering pipeline 412 a, 412 b, and412 c, in order to create a blending profile for each pipelines, 414 a,414 b, and 414 c, respectively. The blending profiles for each profilecan then be transmitted to a user, to another system, to a cognitivesolution developer, or to a system administrator for use in answeringnon-test questions within a QA system or set of QA systems. Inembodiments, blending profile can be created for any number of answeringpipelines, in order to facilitate for the comparison of resultantanswers from each.

The following example may be carried out over the system 400 asdescribed in FIG. 4. In an embodiment, let “N” denote the number of testset questions for a given answering pipeline, and let “K” denote ananswering pipeline parameter that indicates the maximum number ofanswers the pipeline is configured to return in response to anyquestion. A cognitive solution developer or other user may be expectedto execute the answering pipeline on the N questions to obtain “T”answers. The value of T is at most N*K, but it may be less if theanswering pipeline returns fewer than K answers for any of thequestions.

The answering pipeline may also provide a confidence value “C” for eachof the T answers. The value space of C is the range 0 to 1, and thevalue assigned to C indicates the answer pipeline's estimate of thelikelihood that the answer is a suitable answer to the test question.

The cognitive solution developer or other user may then engage theservice of a subject matter expert (SME). The SME may rate the degree ofsuitability of each answer for each test question, and thus furtherrefine and validate the results of the particular answering pipeline. Inother embodiments, a prepared answer key may be used. The answer key canbe the same for all pipelines used, and contain an answer or set ofanswers considered to be correct.

The system can then calculate certain metadata for each answeringpipeline, prior to the use of the pipeline in [non-test] questionanswering. To assist in calculating the metadata, an internal valuereferred to herein as the correct answer count or “CAC” may bedetermined. In embodiments, the CAC may be determined by first reducingthe value space of the answering ratings, based on a configurablethreshold, to a simple binary scale with “1” indicating a correct answerand “0” indicating an incorrect answer. Thus, the CAC may be the countof how many of the T answers have a 1 rating (i.e., how many of thereturned answers are correct).

The first metadata value calculated, the answer accuracy value, asdescribed herein, may indicate the probability that the answeringpipeline will produce a correct answer to a question. The answeraccuracy value, or “AVV”, may be calculated as the ratio CAC/T (i.e.,correct answers CAC over total answer T), or the number of the T answerswith a “1” rating divided by the number of total number of answers T.

The second metadata value set to be generated, the answer confidencetable, as described herein, may indicate the probability that theanswering pipeline will produce an answer that has a confidence of acertain confidence value. The answer confidence table, or “ACT”, may becomputed using a configurable step value. In embodiments, this value candefault to a step value of 0.01. For each confidence value, or “CV”,from 0 to 1, according to the step value, a number of correct answers,or “NCA”, can be calculated to reflect the number of the T answers thathave a confidence value C of at least CV. The ACT location CV may thenbe associated with NCA/T, denoted ACT[CV]=NCA/T. Thus, each valueACT[CV] (in the ACT) may indicate the probability that the answeringpipeline will produce an answer that has a confidence of at least CV.

The third metadata value set to be generated, the correct answerconfidence table, as described herein, may contain a set of values whicheach indicate that the answering pipeline will produce an answer thathas a confidence of at least a particular confidence value, when theanswering pipeline produces a correct answer. The correct answerconfidence table, or “CACT”, may be computed using a configurable stepvalue. In embodiments, this value can default to a step value of 0.01.For each confidence value CV from 0 to 1 according to the step value,the invention may calculate a number, “NCCA” of the T answers that havea 1 rating and a confidence value C or at least CV. Then, the ACTlocation CV may be associated with NCCA/CAC, denoted ACT[CV]=NCCA/CAC.Thus, each value CACT[CV] may indicate the probability that theanswering pipeline will produce an answer that has a confidence of atleast CV when the answering pipeline produces a correct answer.

FIG. 5 depicts a flow diagram of a method 500 for calibrating a set ofQA pipelines, according to embodiments of the present disclosure. Inembodiments, this calibration may occur via the creation of metadata forblending profiles, as described in FIG. 4. In embodiments, the method500 may begin when a first set of test answer data is generated for afirst answering pipeline, per 501. The test answer data may be theoutput of a QA system, for example, the QA system described in FIG. 3,and may comprise a set of answers to a processed question as well as oneor more confidence values associated with the particular answer. Theanswer data may include other data or metadata, as is relevant to theparticular QA system processing the question and generating the testanswer data.

The system may generate a first blending profile using the first set oftest answer data, per 502. The blending profile may include metadatagenerated by the system using the first set of test answer data,including the answers and the confidence value. In the generation of thefirst blending profile, the system may compare, with an answer key, theanswers in the test answer data.

A second set of test answer data may be generated by use of the secondanswering pipeline, per 503. Using the set of test answer data for thesecond answering pipeline, the system may generate a second blendingprofile, per 504. The second blending profile may comprise metadatavalues including, for example, those described in FIG. 4, such as theanswer accuracy value, an answer confidence table, and the correctanswer confidence table.

Test answer data and blending profiles may be generated for a number ofanswering pipelines, as is appropriate to the particular problem,solution, or QA environment. The blending profiles may then be stored ortransmitted for use in answering a question or set of non-testquestions.

FIG. 6 depicts a diagram of an illustrative embodiment of a system 600for blending answers from a set of answering pipelines, according toembodiments of the present disclosure. In embodiments, a question 602may be provided to the answering pipelines 604 for processing. Theresult of the processing may be a set of question answer data,including, for example an answer or set of answers (“A”) and aconfidence value for each answer (“C”). The system may obtain blendingprofiles 606 for each answering pipeline. In embodiments, the elementsdescribed in FIG. 6 including the answering pipelines 604 and theblending profiles 606 may be analogous to those described in FIG. 4,including for example, the answering pipelines 412 and blending profiles414. The question 602 may be processed in a similar manner as a questionfrom the set of test questions 402 of system 400 in FIG. 4. However, thequestion in FIG. 6 may be an actual (i.e., non-test) question submittedby a user, and the blending profiles can comprise the metadata createdbased on the processes described in FIGS. 4 and 5.

In embodiments, the “blender” 608 may be a computer system for blendingthe answers from numerous pipelines 604. The blender 608 may receive thequestion answer data from each of the pipelines 604, along with theblending profiles 606 (e.g., blending profiles created via generation ofmetadata value sets as described in FIGS. 4 and 5). The blender 608 maythen calculate a vote weight for each answer from each of the pipelines610, using the metadata in the blending profile 606. In one embodiment,blender 608 is configured to only retain from each answering pipelinethe J answers with the highest confidence value, for some configurablevalue J.

In some embodiments, the vote weight can be calculated for each answer.A vote weight of each answer may represent the probability that it is acorrect answer given that the answering pipeline assigned it aconfidence of at least C′. The vote weight may be calculated in someembodiments using the formula: CACT[C′]*AVV/ACT[C′], as depicted at 610a. Each of these values may be determined using the metadata calculatedfor each answering pipeline blending profile. As described herein,CACT[C′] may be a particular confidence “C′” location on the correctanswer confidence table. AVV may be the answer accuracy value,calculated as described herein; and ACT[C] may be a particular locationon the answer confidence table keyed using the particular confidence“C′”. In embodiments, C′ may be the answer's confidence (as provided bythe answering pipelines 604) truncated to the nearest confidence stepvalue. For example, if the answering pipeline provided answer confidenceC was 0.926 and the step value was 0.01, then C′ could be 0.92. Thegenerated metadata enables the formula to calculate an answer's voteweight based on the probability that its confidence is at least C′(assuming the answer is correct), multiplied by the probability of theanswer being correct, and then divided by the probability that itsconfidence is at least C′.

In embodiments, the blender 608 may then select a top answer or list oftop answers based on the calculated vote weights, 612. In oneembodiment, blender 608 may combine the answers from all the answeringpipelines by sorting the answers from greatest to least vote weight andthe selecting the top “K” answers (where “K” is based on, for example, aconfigurable setting) from the beginning of the sorted list. In anotherembodiment, blender 608 selects the answer with the highest vote weightfrom all answer pipeline lists, adds it to the output list, and thenremoves it from the answer pipeline list. This sequence may be iterated“K” times to select the top “K” answers. The blender 608 can thenoutput, to a user or other system, the “K” top answers 614. The answermay be output by the blender 608 in the form depicted at 616 toincluding the answer “A” and the vote weight “VW”. In this way, theanswers output by each of the answering pipelines 604 may be comparedamongst one another, regardless of the processing used by eachparticular answering pipeline 604.

FIG. 7 depicts a flow diagram of a method for blending answers from aset of QA pipelines, according to embodiments of the present disclosure.At 701, a system may generate a first set of question answer data usinga first answering pipeline. This question answer data may result fromthe processing of a question or query by the first answering pipeline.The question answer data may include a set of one or more answers to thequestion, as determined by the first answering pipeline. The questionanswer data may also include a confidence value for each answergenerated by the first answering pipeline. As described herein, theconfidence value may indicate the answering pipeline's estimate of thelikelihood that the answer is a suitable answer to the question. Thesystem may then generate a second set of question answer data using asecond answering pipeline, per 702. This process may mirror that of thegeneration of the question answer data for the first answering pipeline,as described at 701. In embodiments, the process of question answer datageneration may be repeated for each pipeline, as determined by thenumber of different pipelines being utilized by the QA blending system.

The QA blending system can then determine a vote weight for the answersin the first set of question answer data, per 703. The system candetermine the vote weight using a weighting formula, for example theweighting formula described in FIG. 6, and the first blending profile.The system can then determine a vote weight for each of the answers inthe second set of question answer data, using the same weighting formulaas used for the first set of question answer data and the secondblending profile, per 704. The system can then select an answer or setof answers with the highest vote weight or highest vote weights fromamong all the answer data, per 705. This answer or set of answers can bepresented or returned to a user, per 706. In embodiments, the answer orset of answers can each be presented with its corresponding vote weightor their corresponding vote weights.

In embodiments, a question classifier may be used in conjunction withthe QA blending system described herein. The question classifier may beintegrated with the system's overall processing of the question, inorder to provide a more precise confidence value based on a type orclass of question.

In embodiments, the question classifier may be integrated with the QAsystem by generating a question classifier confidence value for eachquestion in the set of test questions for each pipeline. For example,question classifier confidence values A1, B1, and C1, may be calculatedfor each question A, B, and C in the set of test questions, in relationto the first answering pipeline. For each question, the questionclassifier confidence value can be combined with the confidence value ofeach answer from the pipeline, such as by multiplying them, to produce arevised confidence value for the answer in the test answer data for thepipeline. This process can be repeated for the second answering pipeline(and any additional answering pipelines). In this way, the revisedconfidence value for each answer takes into account the type of questionbeing answered and the extent to which the QA system's questionclassifier has been trained to prefer the answers from a particularanswering pipeline for a particular question type.

In other embodiments, the system may generate a first questionclassifier confidence value for the question submitted to the system.The question classifier could determine a confidence that the questionis associated with one or more question types. In embodiments, eachquestion type could be associated with a distinct answering pipeline.Thus, the question classifier confidence value produced by the questionclassifier could indicate a determined level of appropriateness that theparticular answering pipeline is the correct pipelines to be used inanswering the question. In embodiments, the data contained within thequestion answer data set may be modified based on the use of a questionclassifier.

For example, the first answering pipeline may generate a set of questionanswer data, in response to receiving a question. The questionclassifier confidence value may be determined for the question, asdescribed above. The question classifier confidence value may then becombined with the confidence value in the question answer data generatedby the first answering pipeline, such as by multiplying them, to producea revised confidence value in the question answer data. Similarly, thesecond answering pipeline may combine a generated question classifierconfidence value with the standard confidence value. This new value(question classifier confidence value combined with second answeringpipeline answering confidence value) may replace the confidence value inthe generation of the question answer data set. In this way, thequestion classifier may impact the determined confidence of an answer,when used with the disclosed QA system blending.

In other embodiments, the vote weight as described, for example, at FIG.6, may be impacted by the question classifier. As noted, the vote weightfor a particular answer in a particular pipeline may be calculated bymultiplying the CACT probability/ACT probability ratio by the particularAAV. When using a question classifier, the product of the aforementionedmay be further multiplied by the confidence value obtained from thequestion classifier for the particular question type associated with aparticular answering pipeline. This process may be repeated for each ofthe pipelines, in order to impact the vote weights of each of theanswers from each of the pipelines.

FIG. 10 depicts the representative major components of an examplecomputer system 1000 that may be used, in accordance with embodiments ofthe present disclosure. It is appreciated that individual components mayvary in complexity, number, type, and\or configuration. The particularexamples disclosed are for example purposes only and are not necessarilythe only such variations. The computer system 1000 may comprise aprocessor 1010, memory 1020, an input/output interface (herein I/O orI/O interface) 1030, and a main bus 1040. The main bus 1040 may providecommunication pathways for the other components of the computer system1000. In some embodiments, the main bus 1040 may connect to othercomponents such as a specialized digital signal processor (notdepicted).

The processor 1010 of the computer system 1000 may be comprised of oneor more cores 1012A, 1012B, 1012C, 1012D (collectively 1012). Theprocessor 1010 may additionally include one or more memory buffers orcaches (not depicted) that provide temporary storage of instructions anddata for the cores 1012. The cores 1012 may perform instructions oninput provided from the caches or from the memory 1020 and output theresult to caches or the memory. The cores 1012 may be comprised of oneor more circuits configured to perform one or methods consistent withembodiments of the present disclosure. In some embodiments, the computersystem 1000 may contain multiple processors 1010. In some embodiments,the computer system 1000 may be a single processor 1010 with a singularcore 1012.

The memory 1020 of the computer system 1001 may include a memorycontroller 1022. In some embodiments, the memory 1020 may comprise arandom-access semiconductor memory, storage device, or storage medium(either volatile or non-volatile) for storing data and programs. Inembodiments, the blending profiles may be recalled from memory for useby the system in pipeline blending. In some embodiments, the memory maybe in the form of modules (e.g., dual in-line memory modules). Thememory controller 1022 may communicate with the processor 1010,facilitating storage and retrieval of information in the memory 1020.The memory controller 1022 may communicate with the I/O interface 1030,facilitating storage and retrieval of input or output in the memory1020.

The I/O interface 1030 may comprise an I/O bus 1050, a terminalinterface 1052, a storage interface 1054, an I/O device interface 1056,and a network interface 1058. The I/O interface 1030 may connect themain bus 1040 to the I/O bus 1050. The I/O interface 1030 may directinstructions and data from the processor 1010 and memory 1020 to thevarious interfaces of the I/O bus 1050. The I/O interface 1030 may alsodirect instructions and data from the various interfaces of the I/O bus1050 to the processor 1010 and memory 1020. The various interfaces mayinclude the terminal interface 1052, the storage interface 1054, the I/Odevice interface 1056, and the network interface 1058. In someembodiments, the various interfaces may include a subset of theaforementioned interfaces (e.g., an embedded computer system in anindustrial application may not include the terminal interface 1052 andthe storage interface 1054).

Logic modules throughout the computer system 1000—including but notlimited to the memory 1020, the processor 1010, and the I/O interface1030—may communicate failures and changes to one or more components to ahypervisor or operating system (not depicted). The hypervisor or theoperating system may allocate the various resources available in thecomputer system 1000 and track the location of data in memory 1020 andof processes assigned to various cores 1012. In embodiments that combineor rearrange elements, aspects and capabilities of the logic modules maybe combined or redistributed. These variations would be apparent to oneskilled in the art.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 8, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 8 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 9, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 8) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 9 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and QA system blending 96.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method comprising: generating, for a question,a first set of question answer data using a first answering pipeline;generating, for the question, a second set of question answer data usinga second answering pipeline; determining, using a weighting formula anda first blending profile associated with the first answering pipeline, afirst vote weight for an answer in the first set of question answer datathat was assigned a first pipeline highest confidence value by the firstanswering pipeline; determining, using the weighting formula and asecond blending profile associated with the second answering pipeline, asecond vote weight for an answer in the second set of question answerdata that was assigned a second pipeline highest confidence value by thesecond answering pipeline; and selecting, as a first answer to thequestion, the answer with an overall highest vote weight from among theanswers in the first set of question answer data and in the second setof question answer data, wherein the weighting formula is used todetermine the first vote weight by: obtaining a first correct answerconfidence table (CACT) probability by searching a first CACT, the firstCACT probability associated with a greatest confidence value that doesnot exceed the confidence value of the answer, the confidence value ofthe answer assigned by the first answering pipeline; obtaining a firstanswer confidence table (ACT) probability by searching a first ACT, thefirst ACT probability associated with a greatest confidence value thatdoes not exceed the confidence value of the answer; calculating a firstratio of the first CACT probability divided by the first ACTprobability; calculating a first product of the ratio multiplied by afirst answer accuracy value (AAV); and assigning the first product asthe first vote weight.
 2. The method of claim 1 wherein the firstblending profile comprises metadata including: the first AAV reflectinga first probability that the first answering pipeline will produce afirst correct answer to the question; the first ACT reflecting a firstset of confidence threshold value and probability association pairs,each first pair indicating a probability that the first answeringpipeline will produce an answer with a confidence value exceeding anassociated confidence threshold value; and the first CACT reflecting asecond set of confidence threshold value and probability associationpairs, each second pair indicating a correct answer probability that thefirst answering pipeline will produce a correct answer with a secondconfidence value equal to or greater than the associated secondconfidence threshold value.
 3. The method of claim 2, wherein the secondblending profile comprises metadata including: a second AAV, the secondAAV reflecting a second probability that the second answering pipelinewill produce a second correct answer to the question; a second ACT, thesecond ACT reflecting a first set of confidence threshold value andprobability association pairs, each first pair indicating a probabilitythat the second answering pipeline will produce an answer with aconfidence value exceeding an associated confidence threshold value; anda second CACT, the second CACT reflecting a second set of confidencethreshold value and probability association pairs, each second pairindicating a correct answer probability that the second answeringpipeline will produce a correct answer with a second confidence valueequal to or greater than the associated second confidence thresholdvalue.
 4. The method of claim 3, wherein the weighting formula is usedto determine the second vote weight of the answer in the second set ofquestion answer data that was assigned the highest confidence value bythe second answering pipeline by: obtaining a second CACT probability bysearching the second CACT for a CACT probability; obtaining a second ACTprobability by searching the second ACT for an ACT probability;calculating a second ratio of the second CACT probability divided by thesecond ACT probability; calculating a second product of the second ratiomultiplied by the second AAV; and assigning the second product as thesecond vote weight.
 5. The method of claim 4, wherein: calculating thefirst product comprises multiplying the first ratio, the first AAV, anda first confidence value obtained by operating a question classifier onthe question; and calculating the second product comprises multiplyingthe second ratio, the second AAV, and a second confidence value obtainedby operating the question classifier on the question.
 6. The method ofclaim 1, further comprising: determining, using the weighting formulaand the first blending profile associated with the first answeringpipeline, a vote weight for each of a first plurality of answers in thefirst set of question answer data, wherein the first plurality ofanswers were assigned the highest confidence values by the firstanswering pipeline; determining, using the weighting formula and thesecond blending profile associated with the second answering pipeline, avote weight for each of a second plurality of answers in the second setof question answer data, wherein the second plurality were assigned thehighest confidence values by the second answering pipeline; andselecting a set of answers to the question, the set of answers to thequestion having the highest vote weights from among the first pluralityof answers and the second plurality of answers.
 7. The method of claim1, wherein: the generating, for the question, the first set of questionanswer data using the first answering pipeline further comprisescombining a first question classifier confidence value with each of thefirst pipeline confidence values with each answer in a first set ofanswers; and generating, for the question, the second set of questionanswer data using the second answering pipeline further comprisescombining a second question classifier confidence value with each of thesecond pipeline confidence values with each answers in a second set ofanswers.
 8. A system comprising: a computer readable storage medium withprogram instructions stored thereon; and one or more processorsconfigured to execute the program instructions to perform a methodcomprising: generating, for a question, a first set of question answerdata using a first answering pipeline; generating, for the question, asecond set of question answer data using a second answering pipeline;determining, using a weighting formula and a first blending profileassociated with the first answering pipeline, a first vote weight for ananswer in the first set of question answer data that was assigned afirst pipeline highest confidence value by the first answering pipeline;determining, using the weighting formula and a second blending profileassociated with the second answering pipeline, a second vote weight foran answer in the second set of question answer data that was assigned asecond pipeline highest confidence value by the second answeringpipeline; and selecting, as a first answer to the question, the answerwith an overall highest vote weight from among the answers in the firstset of question answer data and in the second set of question answerdata, wherein the weighting formula is used to determine the first voteweight by: obtaining a first correct answer confidence table (CACT)probability by searching a first CACT, the first CACT probabilityassociated with a greatest confidence value that does not exceed theconfidence value of the answer, the confidence value of the answerassigned by the first answering pipeline; obtaining a first answerconfidence table (ACT) probability by searching a first ACT, the firstACT probability associated with a greatest confidence value that doesnot exceed the confidence value of the answer; calculating a first ratioof the first CACT probability divided by the first ACT probability;calculating a first product of the ratio multiplied by a first answeraccuracy value (AAV); and assigning the first product as the first voteweight.
 9. The system of claim 8, wherein the first blending profilecomprises metadata including: the first AAV reflecting a firstprobability that the first answering pipeline will produce a firstcorrect answer to the question; the first ACT reflecting a first set ofconfidence threshold value and probability association pairs, each firstpair indicating a probability that the first answering pipeline willproduce an answer with a confidence value exceeding an associatedconfidence threshold value; and the first CACT reflecting a second setof confidence threshold value and probability association pairs, eachsecond pair indicating a correct answer probability that the firstanswering pipeline will produce a correct answer with a secondconfidence value equal to or greater than the associated secondconfidence threshold value.
 10. The system of claim 9, wherein thesecond blending profile comprises metadata including: a second AAV, thesecond AAV reflecting a second probability that the second answeringpipeline will produce a second correct answer to the question; a secondACT, the second ACT reflecting a first set of confidence threshold valueand probability association pairs, each first pair indicating aprobability that the second answering pipeline will produce an answerwith a confidence value exceeding an associated confidence thresholdvalue; and a second CACT, the second CACT reflecting a second set ofconfidence threshold value and probability association pairs, eachsecond pair indicating a correct answer probability that the secondanswering pipeline will produce a correct answer with a secondconfidence value equal to or greater than the associated secondconfidence threshold value.
 11. The system of claim 10, wherein theweighting formula is used to determine the second vote weight of theanswer in the second set of question answer data that was assigned thehighest confidence value by the second answering pipeline by: obtaininga second CACT probability by searching the second CACT for a CACTprobability; obtaining a second ACT probability by searching the secondACT for a ACT probability; calculating a second ratio of the second CACTprobability divided by the second ACT probability; calculating a secondproduct of the second ratio multiplied by the second; and assigning thesecond product as the second vote weight.
 12. The system of claim 11,wherein: calculating the first product comprises multiplying the firstratio, the first AAV, and a first confidence value obtained by operatinga question classifier on the question; and calculating the secondproduct comprises multiplying the second ratio, the second AAV, and asecond confidence value obtained by operating the question classifier onthe question.
 13. The system of claim 8, wherein the method furthercomprises: determining, using the weighting formula and the firstblending profile associated with the first answering pipeline, a voteweight for each of a first plurality of answers in the first set ofquestion answer data, wherein the first plurality of answers wereassigned the highest confidence values by the first answering pipeline;determining, using the weighting formula and the second blending profileassociated with the second answering pipeline, a vote weight for each ofa second plurality of answers in the second set of question answer data,wherein the second plurality were assigned the highest confidence valuesby the second answering pipeline; and selecting a set of answers to thequestion, the set of answers to the question having the highest voteweights from among the first plurality of answers and the secondplurality of answers.
 14. A computer program product comprising acomputer readable storage medium having program instructions embodiedtherewith, wherein the computer readable storage medium is not atransitory signal per se, the program instructions executable by acomputer processing circuit to cause the circuit to perform the methodcomprising: generating, for a question, a first set of question answerdata using a first answering pipeline; generating, for the question, asecond set of question answer data using a second answering pipeline;determining, using a weighting formula and a first blending profileassociated with the first answering pipeline, a first vote weight for ananswer in the first set of question answer data that was assigned afirst pipeline highest confidence value by the first answering pipeline;determining, using the weighting formula and a second blending profileassociated with the second answering pipeline, a second vote weight foran answer in the second set of question answer data that was assigned asecond pipeline highest confidence value by the second answeringpipeline; and selecting, as a first answer to the question, the answerwith an overall highest vote weight from among the answers in the firstset of question answer data and in the second set of question answerdata, wherein the weighting formula is used to determine the first voteweight by: obtaining a first correct answer confidence table (CACT)probability by searching a first CACT, the first CACT probabilityassociated with a greatest confidence value that does not exceed theconfidence value of the answer, the confidence value of the answerassigned by the first answering pipeline; obtaining a first answerconfidence table (ACT) probability by searching a first ACT, the firstACT probability associated with a greatest confidence value that doesnot exceed the confidence value of the answer; calculating a first ratioof the first CACT probability divided by the first ACT probability;calculating a first product of the ratio multiplied by a first answeraccuracy value (AAV); and assigning the first product as the first voteweight.
 15. The computer program product of claim 14, wherein the firstblending profile comprises metadata including: the first AAV reflectinga first probability that the first answering pipeline will produce afirst correct answer to the question; the first ACT reflecting a firstset of confidence threshold value and probability association pairs,each first pair indicating a probability that the first answeringpipeline will produce an answer with a confidence value exceeding anassociated confidence threshold value; and the first CACT reflecting asecond set of confidence threshold value and probability associationpairs, each second pair indicating a correct answer probability that thefirst answering pipeline will produce a correct answer with a secondconfidence value equal to or greater than the associated secondconfidence threshold value.
 16. The computer program product of claim15, wherein the second blending profile comprises metadata including: asecond AAV, the second AAV reflecting a second probability that thesecond answering pipeline will produce a second correct answer to thequestion; a second ACT, the second ACT reflecting a first set ofconfidence threshold value and probability association pairs, each firstpair indicating a probability that the second answering pipeline willproduce an answer with a confidence value exceeding an associatedconfidence threshold value; and a second CACT, the second CACTreflecting a second set of confidence threshold value and probabilityassociation pairs, each second pair indicating a correct answerprobability that the second answering pipeline will produce a correctanswer with a second confidence value equal to or greater than theassociated second confidence threshold value.
 17. The computer programproduct of claim 16, wherein the weighting formula is used to determinethe second vote weight of the answer in the second set of questionanswer data that was assigned the highest confidence value by the secondanswering pipeline by: obtaining a second CACT probability by searchingthe second CACT for a CACT probability; obtaining a second ACTprobability by searching the second ACT for a ACT probability;calculating a second ratio of the second CACT probability divided by theACT probability; calculating a second product of the second ratiomultiplied by the second AAV; and assigning the second product as thesecond vote weight.
 18. The computer program product of claim 17,wherein: calculating the first product comprises multiplying the firstratio, the first AAV, and a first confidence value obtained by operatinga question classifier on the question; and calculating the secondproduct comprises multiplying the second ratio, the second AAV, and asecond confidence value obtained by operating the question classifier onthe question.
 19. The computer program product of claim 14, wherein themethod further comprises: determining, using the weighting formula andthe first blending profile associated with the first answering pipeline,a vote weight for each of a first plurality of answers in the first setof question answer data, wherein the first plurality of answers wereassigned the highest confidence values by the first answering pipeline;determining, using the weighting formula and the second blending profileassociated with the second answering pipeline, a vote weight for each ofa second plurality of answers in the second set of question answer data,wherein the second plurality were assigned the highest confidence valuesby the second answering pipeline; and selecting a set of answers to thequestion, the set of answers to the question having the highest voteweights from among the first plurality of answers and the secondplurality of answers.
 20. The computer program product of claim 14,wherein: the generating, for the question, the first set of questionanswer data using the first answering pipeline further comprisescombining a first question classifier confidence value with each of thefirst pipeline confidence values with each answer in a first set ofanswers; and generating, for the question, the second set of questionanswer data using the second answering pipeline further comprisescombining a second question classifier confidence value with each of thesecond pipeline confidence values with each answers in a second set ofanswers.